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    Time-series anomaly prediction and alert
    2.
    发明授权
    Time-series anomaly prediction and alert 有权

    公开(公告)号：US12293320B2

    公开(公告)日：2025-05-06

    申请号：US17231057

    申请日：2021-04-15

    Applicant: BUSINESS OBJECTS SOFTWARE LTD.

    Inventor： Jacques Doan Huu

    IPC: G06F17/15 ,  G06F18/2433 ,  G06N20/00 ,  G06Q10/04 ,  G06Q10/0635 ,  G06Q10/067

    Abstract: Provided is a system and method which can identify a causal relationship for anomalies in a time-series signal based on co-occurring and preceding anomalies in another time-series signal. In one example, the method may include identifying a recurring anomaly within a time-series signal of a first data value, determining a time-series signal of a second data value that is a cause of the recurring anomaly in the time-series signal of the first data value based on a preceding and co-occurring anomaly in the time-series signal of the second data value, and storing a correlation between the preceding and co-occurring anomaly in the time-series signal of the second data value and the recurring anomaly in the time-series signal of the first data value.

    Feature selection for model training
    3.
    发明授权
    Feature selection for model training 有权

    公开(公告)号：US12271797B2

    公开(公告)日：2025-04-08

    申请号：US17313460

    申请日：2021-05-06

    Applicant: BUSINESS OBJECTS SOFTWARE LTD.

    Inventor： Louis Desreumaux ,  Jacques Doan Huu

    IPC: G06N20/20 ,  G06N5/01

    Abstract: Systems and methods include determination of a first plurality of sets of data, each including values associated with respective ones of a first plurality of features, partial training of a first machine-learning model based on the first plurality of sets of data, determination of one or more of the first plurality of features to remove based on the partially-trained first machine-learning model, removal of the one or more of the first plurality of features to generate a second plurality of sets of data, partial training of a second machine-learning model based on the second plurality of sets of data, determination that a performance of the partially-trained second machine-learning model is less than a threshold, addition, in response to the determination, of the one or more of the first plurality of features to the second plurality of sets of data, and training of the partially-trained first machine-learning model based on the first plurality of sets of data.

    Determining component contributions of time-series model
    4.
    发明授权
    Determining component contributions of time-series model 有权

    公开(公告)号：US12159240B2

    公开(公告)日：2024-12-03

    申请号：US17233600

    申请日：2021-04-19

    Applicant: BUSINESS OBJECTS SOFTWARE LTD.

    Inventor： Jacques Doan Huu ,  Elouan Argouarch

    IPC: G06N5/04 ,  G06N20/00

    Abstract: Provided is a system and method which decomposes a predicted output signal of a time-series forecasting model into a plurality of sub signals that correspond to a plurality of components, and determines and displays a global contribution of each component. In one example, the method may include iteratively predicting an output signal of a time-series data value via execution of a time-series model, decomposing the predicted output signal into a plurality of component signals corresponding to a plurality of components of the time-series machine learning algorithm, respectively, and displaying the plurality of global values via a user interface.

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